Claude Haiku 5.5 (1587 pts) is a +257pt improvement over Haiku 4.5 (1330 pts)! It also significantly improved across all categories. Dive into the Code Arena: WebDev at https://arena.ai/leaderboard/code/webdev
JetBrains has released Mellum2.1, a 12B mixture-of-experts thinking model with 2.5B active parameters, under Apache 2.0 on Hugging Face. Post-training reinforcement learning in real software repositories raised SWE-bench Verified from 2.0 to 47.0, according to JetBrains' self-reported results. Qwen3.5-9B still leads on SWE-bench Pro, GPQA Diamond and AIME, and GGUF builds start at 7.0 GB for local use.
Anthropic released Claude Haiku 5.5, raising its OSWorld 2.1 score from 15.7% to 72.4% and supporting a 1 million token context window. The article notes Haiku 5.5 still scores 39.2% on Terminal-Bench 4.0 against Sonnet 5.5's 70.6%, and that prompts above 100,000 tokens are priced higher, so migration costs need to be measured on real workloads.
Across StepFun's 8-benchmark eval, Step 5 Preview beats Kimi K3 and GLM-5.3 on 6, including DeepSWE (67.7), ProgramBench (80.5), StepCodeBench (49.0), and FrontierFinance (66.4).
Step 5 Preview from @StepFun_ai is live on OpenRouter. Their new flagship for agentic work: a sparse MoE (27B active, 600B total), 1M context, and text, image, and video input. Strong at coding and professional knowledge work, especially finance. Try it: https://openrouter.ai/stepfun/step-5-preview
StepFun has released Step 5 Preview, its flagship model for agentic work, built on a sparse Mixture-of-Experts architecture with 27B active and 600B total parameters. The source says it performs strongly in software engineering and professional tasks, but the feed supplied only an excerpt, so benchmark details are not available here.
JetBrains released Mellum2.1, a 12B mixture-of-experts model with 2.5B active parameters under the Apache 2.0 license, built for coding agents. Post-training shifted to reinforcement learning across thousands of environments and millions of sandboxed runs, and the model is available on Hugging Face. The source reports gains over Mellum2 on LiveCodeBench, AIME, GPQA Diamond, BFCL v4, IFEval, and SWE-bench Verified, and says it serves almost twice the tokens of Qwen3.5-9B under heavy load.
AIWhy it matters: The post shows how reinforcement learning in real sandboxed environments changed a compact open model's repository work, with benchmark gains against Mellum2 and two peers.
Super glad to push forward with a new day0 collaboration with @aimlapi ! 🤠🤘 Have fun running your favorite agentic and cowork scenario with it!
Anthropic released Claude Haiku 5.5, priced the same as OpenAI's GPT-6 Luna, with a 1M-token context window. Artificial Analysis scored it 43 on its Intelligence Index, slightly ahead of GPT-6 Luna at 38, but it uses about 3x more output tokens at max effort.
Anthropic released Claude Haiku 5.5, which it calls the fastest, cheapest, and most capable Haiku model so far. On average it costs about 75% less to run than Haiku 4.5, with input at $0.10 and output at $0.50 per million tokens for requests up to 100,000 tokens. Anthropic also cut Sonnet 5.5's cache read price from $0.20 to $0.10 per million tokens, which it says lowers run costs by about 20% on many agent tasks.
Read more at https://devin.ai/blog/claude-haiku-5-5
For reference, Haiku 4.5 came out on October 15th, 2025 so these two columns are less than a year apart. and oh yeah Haiku 5.5 is also much faster and 75% cheaper...
Anthropic's Claude Haiku 5.5 is now available on Amazon Bedrock and Claude Platform on AWS. According to Anthropic, it is the fastest and most efficient model in the Claude 5.5 family and costs around 75 percent less than Claude Haiku 4.5 for most tasks. The post also covers pairing it with Claude Opus 5.5 as a subagent layer and provides Boto3, Converse, and Anthropic SDK examples for calling the model.
Anthropic has released Claude Haiku 5.5, which the author describes as its fastest and cheapest model to date. The source says it costs about 75% less to run than Claude Haiku 4.5 and is the first Haiku model with an adjustable effort setting. The attached benchmark table reports Haiku 5.5 scores on tasks including computer use (OSWorld 2.1 offline subset, 72.4%) and Terminal-Bench 4.0 (39.2%), compared with Haiku 4.5 and other models.
Haiku 5.5 is designed for high-volume, cost-sensitive tasks. It reliably handles repetitive work like summaries and classification, and pairs well with Claude Opus 5.5 and Sonnet 5.5 as a subagent on coding work. It’s also fast enough for live customer support and browser use.
Haiku 5.5 is a significant step up over Haiku 4.5 across coding, computer use, and knowledge work.
Mistral Large 4 against the field: efficiently reasoning over diverse complex challenges. CTF speedrun: 19 challenges, with tool calls and solve times grounded in actual runs. It solved 18 of the 19.
Mistral AI's Mistral Large 4 is listed on OpenRouter as a frontier multimodal model accepting text and image input. The listing says it is built for reasoning, coding, and agentic workloads and offers a 1M-token context window. The feed excerpt is truncated, so further details such as pricing or availability are not confirmed here.
On human evaluation, the model performs better than GLM 5.3 on STEM, CAD, and finance tasks, and performs on par on agentic coding. 5/n
On important coding and agentic benchmarks such as DeepSWE, AutomationBench, and AA-Briefcase, ML4 matches the performance of the best open-weight models. It is SOTA on finance and legal workflows, as well as on complex multimodal grounding benchmarks. It can navigate complex terminal workflows, work across spreadsheets, slides, and PDFs, and reason over scientific and multimodal tasks. 4/n
Reflection announced Beam, a text-only 501B-total, 23B-active MoE model for coding, agentic, and scientific work, trained from scratch with full weights under Apache 2.0 promised this month. Self-reported results include 80.9 on SWE-bench Verified and 3–4x the inference efficiency of GLM 5.2, while the roundup notes that GLM 5.3, Kimi K3, Qwen 3.8 Max, and DeepSeek V4.1 Flash are generally ahead.
Reflection AI, an Nvidia-backed startup, released Beam, its first open-weight large model, aimed at coding and agent tasks. The company says Beam is comparable to Z.ai's GLM-5.2 and is approaching Qwen3.8-Max on coding and agent work. Beam has 501 billion total parameters, with 23 billion activated per task in a sparse architecture.
The author says Beam, a 501B-parameter open model from Reflection AI, comes close to GLM 5.2 in capability but trails GLM 5.3, Kimi K3, and DeepSeek V4.1 Flash in several areas. The author attributes Beam's competitiveness mainly to inference efficiency, with inference compute at roughly one-third to one-quarter of GLM 5.2's.
The model behind this result is now on @huggingface 🤗 Nemotron-Labs-3-Competitive-Coding is a competitive-programming specialist model based on Nemotron-3-Ultra. https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4
Excited about the new open model from @reflection_ai! More US models coming to Ollama!
Congrats to Reflection folks -- it's hard to get the first model out, and we hope you can rapidly accelerate contributions to the ecosystem from here :)
Pareto 26.10 Preview is a multimodal composite model built for research, coding, and agentic workflows. It is described as delivering frontier-level performance across a broad range of general-purpose tasks, though the source excerpt is a preview and provides no benchmark scores, parameter counts, pricing, or availability details.
Google has announced Gemini 4 Argon, initially available only to trusted cyber defenders through its Fairwind Program while US government approval is pending. The author says the model is aimed at long-running software engineering, enterprise knowledge work, and cybersecurity tasks, with a 1 million token output limit. The post also gives promotional pricing of $2 per million input tokens and $10 per million output tokens, rising to $4 and $20 afterward, alongside a benchmark comparison.
AIWhy it matters: The post places Gemini 4 Argon's benchmark table beside GPT-6 Astra and Claude models, showing where each leads across coding, knowledge work, and cybersecurity tasks.
Google is sharing Gemini 4 Argon first with trusted defenders in its Fairwind Program, with frontier capabilities in coding, knowledge work, and cyber security defense. The model is also being rolled out to the US government, and the company plans broader availability as testing progresses and safeguards allow.
Just announced Gemini 4 Argon, our new frontier model! It delivers frontier performance across the board on complex software tasks. Thousands of Googlers have already been using it in Antigravity internally and we’re excited by the feedback. It’s rolling out first to a set of trusted cyber defenders in our Fairwind Program with broader availability coming as soon as possible.
Google AI announced Gemini 4 Argon, a new frontier model built for deep reasoning across long, complex workflows in software engineering, legal and finance knowledge work, and cybersecurity defense. Google says it is expanding the model's output token limit to 1M tokens. Argon is rolling out first to trusted cyber defenders in the Fairwind Program, with broader availability to follow as soon as possible.
AIWhy it matters: The benchmark table compares Gemini 4 Argon against GPT-6 Astra and Claude models across knowledge work, coding, and multimodal tasks, showing where it leads and trails.
Google DeepMind announced Gemini 4 Argon, rolling out first to trusted cyber defenders through its Fairwind Program. Argon will launch at an introductory price of $2 per million input tokens and $10 per million output tokens, with output limits raised to 1M tokens. The post cites a 77.9% score on DeepSWE v1.1 and 91.7% on LVBench, and says broad availability will follow safeguard testing.
AIWhy it matters: The post pairs Argon's benchmark claims with the phased release, pricing, and safeguard details, helping readers weigh its frontier-level capabilities against its access limits.
Google announced Gemini 4 Argon, a new frontier model rolling out first to trusted cyber defenders through its Fairwind Program. The model's output limit rises to 1M tokens from 64K, and its introductory API price is $2 per million input tokens and $10 per million output tokens. Google says broader availability to developers, enterprises, and consumers will follow after more testing of guardrails.
AIWhy it matters: The post pairs benchmark claims with a phased access plan, pricing, and safety measures, which helps readers judge how quickly Argon may reach developers.
BAAI released AREX-2, a 27B-parameter long-horizon agent model that improves solutions over multiple test-time rounds by proposing, measuring, reflecting, and revising. It was trained on machine-learning and algorithmic-programming tasks with verifiable feedback, and the source reports that this self-improvement transfers to deep research. The model is Apache License 2.0 licensed and has a 262,144-token context length.
AIWhy it matters: The source compares AREX-2 against closed and open models on coding and deep-research benchmarks, showing how test-time self-improvement is measured across task types.
ModelScope announced IQuest-Q1, a 320B MoE model with 15B active parameters and a 512K context window for agentic coding. The post reports scores of 84.5 on CyberGym, 83.2 on Terminal-Bench 2.1, 64.6 on DeepSWE v1.1, and 63.0 on NL2Repo, and says weights are released under the IQuest-Q1 License.
Anthropic's Cat Wu says Claude Sonnet 5.5 lets Claude Code users complete about 30% more tasks than with Sonnet 5. The model needs fewer tokens for the same work, and in a leaf-raking tool-call demo it finished 24 seconds faster using 6K fewer tokens.
AIWhy it matters: The post gives a measured Claude Code task-completion gain and a token-use example, showing what the model upgrade means for a coding agent workflow.
Sonnet 5.5 fixing a bug with Claude Code. 30% faster and 30% less usage.
Anthropic launched Sonnet 5.5, which the post says is smarter and more tasteful than Sonnet 5. It is positioned for work that does not need the extra capability of Opus or Fable.
OpenAI announced GPT-6 Sol and GPT-6 Luna, rolling out today in ChatGPT Work and Codex. The rollout covers Plus, Pro, Business, Enterprise, and Edu users.
Anthropic has launched Claude Opus 5.5, the first model in its new Claude 5.5 family. According to the quoted launch post, it performs at the level of Claude Fable 5.1 for most tasks and costs 40% less to run than Opus 5. The author says it leads in coding and knowledge work and praises its writing quality.
AIWhy it matters: The quoted launch post gives a concrete cost comparison, useful for weighing Opus 5.5 against earlier Opus and Fable 5.1 models for routine work.